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Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.
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Set up a browser automation command-line tool for agents to navigate sites, fill forms, extract data, and capture screenshots. It also points to dedicated guides for app testing, Electron desktop apps, and cloud browsers.
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Finds and retrieves paid financial datasets when free sources lack coverage or detail. Guides comparison of providers, success rates, prices, and required inputs, while keeping execution costs and remaining credits visible.
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Choose suitable charts for trends, comparisons, distributions, and relationships, then create them in Python with matplotlib, seaborn, or plotly. Apply guidance on colors, labels, scales, and accessibility to produce publication-quality figures or interactive charts.
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Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.
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Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.
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Develop and verify changes to Apache Beam's Python SDK. Set up a virtual environment, run tests, build installable packages or containers, and run data processing pipelines with the modified SDK.
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Audit website images for search visibility and page performance. Check alternative text, filenames, formats, compression, responsive loading, dimensions, image sitemaps, and structured data that helps search engines understand image content.
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Evaluate object detection predictions against labeled ground truth with TAO Data Services. Produce class-level precision, recall, accuracy, and AP, a detection quality measure, with CSV results and precision–recall plots for reviewing model performance.
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Export existing medical AI inference results to MLflow, a system for recording machine learning runs. Preview sanitized metadata, parameters, reproducibility details, and engineering quality metrics before export, with optional image artifacts under explicit upload policies.
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Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
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Combined codebase health dashboard that scores a project across all four quality dimensions — PR quality, architecture, tech debt, and test quality — in a single pass, drawing on twelve classic engineering books. Triggers when: user wants an overall quality assessment, asks "how healthy is this codebase?", "run all the checks", "I need a health score before the release", or wants to onboard a new team with a quality overview. Do NOT trigger for: server health checks, HTTP health endpoints, Kubernetes liveness/readiness probes, database health, or application uptime. Also do not trigger when the user specifically requests only one dimension — use the corresponding focused skill instead (brooks-review / brooks-audit / brooks-debt / brooks-test).
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Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.
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Collaboration platform configuration methodology for legal matter sites. Site architecture, workflow identification, dashboard design, data quality governance, and user adoption for SharePoint, Teams, and equivalent platforms. M365 is the reference implementation — outputs are platform-agnostic enough to brief IT or build simple automations without becoming a Power Automate manual. Use when setting up a matter site, identifying workflows to automate, designing reporting dashboards, managing platform data quality, or driving user adoption. Trigger on: 'set up the matter site', 'configure SharePoint', 'build a dashboard', 'what should we automate', 'brief IT on this workflow', 'nobody is using the platform', 'data quality is poor', 'set up Teams channel', 'matter site structure', 'alerts and notifications', 'user training', 'platform governance', 'status dashboard', 'what workflows can we automate', 'matter site template'.
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Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
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Profile construction data to understand characteristics, distributions, quality metrics, and patterns. Essential for data quality assessment and ETL planning.
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Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
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Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling. Use when a dataset is new, its quality is unknown, or the user requests open-ended profiling; use data-analysis instead for a defined hypothesis or decision question.
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Enrich any entity — companies, domains, emails, or people — with verified data from multiple sources. Combines Apollo, Strale, LinkedIn, and Akta data to fill gaps in your CRM, lead databases, or research datasets with production-quality enrichment.
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Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).
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Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.
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Design a data cleaning and transformation pipeline — missing values, outliers, and deduplication. Use when asked to "clean this dataset", "handle missing values", or "deduplicate this data".
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Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.
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Client reporting, quality assurance, and financial operations management. Handles KPI frameworks, compliance audits, budget tracking, and launch checklists. Use when: managing client dashboards, ensuring brand compliance, conducting QA reviews, tracking marketing spend, ensuring GDPR/CAN-SPAM compliance, handling a request to delete someone's data across the marketing stack, deciding whether an AI voice or AI presenter has to be disclosed, proofreading deliverables. Also triggers on: client reporting, QA, quality assurance, brand compliance, marketing budget, financial tracking, legal compliance, GDPR, CAN-SPAM, proofreading, launch checklist, right to erasure, right to be forgotten, data deletion request, DSAR, someone asked us to delete their data, do we delete or suppress, suppression list, they unsubscribed and we emailed them again, we deleted them and they came back, restored a backup and re-emailed people, custom audience still has deleted contacts, California Delete Act, DROP, data broker deletion, enrichment records disappearing
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